CN112950943A - Transfer station calculation method based on multi-metadata - Google Patents
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- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
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- G08G1/00—Traffic control systems for road vehicles
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- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
Abstract
The invention discloses a transfer station calculation method based on multivariate data, in particular relating to the technical field of urban intelligent public transportation data mining, comprising the steps of S1, acquiring main transfer station pairs, selecting station pairs which are possible to have multi-station transfer, S2, filling lines and station lists of multi-station transfer aiming at passenger transfer habits and transfer psychological factors, S3, calculating transfer proportion of the number of passengers getting off at adjacent stations according to the obtained algorithm transfer distance, S4, training the algorithm based on various bus high and low peak time periods according to the actually measured number of transfers and the type of the transfer station pairs, outputting corresponding training models, the invention can more accurately determine the actual passenger flow transfer information of high and low peaks, is convenient for corresponding transfer facility equipment of departments such as urban construction and traffic management, and the public traffic operation and management departments can carry out more accurate capacity matching, thereby improving important public transportation urban indexes such as public transportation transfer efficiency, passenger satisfaction and the like.
Description
Technical Field
The invention relates to the technical field of urban intelligent public transportation data mining, in particular to a transfer station calculation method based on multivariate data.
Background
For traffic passenger flow analysis, it is very important to obtain the boarding and alighting stations (passenger flow OD analysis) of public traffic passengers. At present, domestic buses generally only use the IC card when getting on the bus and do not use the IC card when getting off the bus, so that the IC card passenger flow data only contains the information of the getting on station and does not contain the information of the getting off station. Therefore, the industry calculates the departure station by combining the traffic travel rule of the passenger in a period of time with the information of the bus GPS, and the specific technical background refers to a pencian paper, namely a public transport passenger flow statistical method based on IC card historical data, Vol.227, No.5,2017: pp.21-24.
In the calculation of a specific getting-off stop, particularly when a bus transfers a track or other lines of the bus, the following problems are generally encountered: can learn the track/bus station that the passenger enters through the track/bus IC card data of punching the card that the passenger changes, but how to judge the exact bus station of getting off of the passenger when the bus route that the passenger gets off has two or more stops that are close to the track/bus route that changes? If the transfer station is wrongly calculated, on one hand, the guarantee and the design of transfer facilities and passages of different stations are unreasonable, and on the other hand, the number of people in the bus is wrongly judged, so that the transport capacity configuration is unreasonable, and the situations of congestion in the bus are caused.
The traditional getting-off station method is to select a bus station closest to a rail station in a straight line distance in a certain bus line around a transferred rail/bus station as the calculation of the getting-off station: for example, a circle is drawn by taking the transformed target station as the center of the circle, and the distances between all stations on the bus line and the center of the circle are matched, so that the bus getting-off station with the closest straight line distance is found out. However, according to actual tests, it is found that when two or more nearby bus stations are located beside a track station, the method has a large error due to constraints such as an actual transfer distance, forward and backward arrival of the bus stations (under the condition of road congestion, a passenger can select a bus station which is far away but is located in the forward direction), peripheral matching completeness (whether a breakfast spot is located at the roadside at an early peak, whether a dish selling spot or a restaurant is located at the roadside at an late peak, whether a public toilet is available, and the like), transfer channel safety (whether a student has a safe street-crossing channel, whether a camera device is provided, whether a person conducts a command when crossing a street, and the like), transfer channel convenience (whether a sunshade pedestrian corridor is provided, whether an escalator is provided in an elevation difference, and no obstacle traffic conditions are provided). The method has large errors, and can cause the estimation error of transfer stations, so that great deviation exists in the aspects of the guarantee of municipal facilities and the like, emergency situation disposal and the like, and further the situations of citizen complaints and the like occur.
Disclosure of Invention
In order to solve the above problems, embodiments of the present invention provide a transfer site calculation method based on metadata, which can implement rotary tillage on land while irrigation, and can improve work efficiency.
In order to achieve the purpose, the invention provides the following technical scheme: a transfer station estimation method based on multivariate data comprises the following steps,
s1, acquiring the main transfer station pairs, and selecting the station pairs where multi-station transfer is possible,
s2, filling in the lines and station lists of multi-station transfer according to the passenger transfer habits and psychological factors,
s3, calculating the transfer proportion of the get-off people at the adjacent station according to the obtained algorithm transfer distance,
and S4, training the algorithm based on various bus high and low peak time periods according to the actually measured transfer number and the type of the transfer station pairs, and outputting a corresponding training model.
Further, the transfer station pair in S1 is obtained by traditional bus IC card swiping data, and counts the card swiping record of the boarding station where the bus IC card is swiped and the boarding record of the buses or tracks taken after transfer.
Further, the line and station list for multi-station transfer is used for counting line numbers of getting-off vehicles before transfer, station names 1 of vehicles before transfer, station numbers 1, station transfer linear distances 1, 2, 3, line numbers of getting-on vehicles after transfer, station numbers and remark items after transfer.
Further, the line number of the getting-off before the transfer is the bus line number of the passenger getting-off; the station name 1 before transfer is the first station name of the transfer condition possibly existing in the getting-off bus line; the station number 1 is the number of the station; the station transfer linear distance 1 is the linear distance between the station and a boarding station, the boarding line number after transfer is the track or bus line number of boarding of passengers, and the boarding station number after transfer is the track or bus station number of boarding.
Further, the passenger transfer habits are transfer behavior habits of passengers in different time periods.
Further, the psychological transfer selection factors comprise actual transfer distance, distance conversion of inter-station vehicle running time, transfer channel convenience, transfer channel safety and peripheral matching completeness.
Further, the safety of the transfer passage is divided into 5 grades of very safe, general, unsafe and dangerous according to the grading standard, and the safety is sequentially graded into 5 grades, 4 grades, 3 grades, 2 grades and 1 grade.
Further, the scoring criteria include the following 5,
the speed of people walking on part of the section of the transfer passage is less than half of the normal 1.2m/s due to the narrowness or congestion, and the number of the people walking is 1-2;
the transfer channel has pedestrian crossing channel, which is easy to have safety accident, no traffic light and other conditions, and the number of the channels is 1 to 3;
potential safety hazards such as falling rocks, falling objects, construction working faces and the like exist on two sides of the transfer passage, and the number of the potential safety hazards is 1-3;
the transfer passage has no street lamp at night, the underground passage has no camera device, and the events such as theft, robbery and the like happen in the near term, and the number of the events is 1 to 3;
the road is uneven, and children or old people fall or are injured due to channel hardware in recent times, and the number of the buttons is 1 to 3.
Further, the station pairs comprise station pairs for transferring buses and station pairs for transferring tracks.
Compared with the prior art, the invention has the technical effects and advantages that:
according to the invention, based on the IC card swiping OD data, the bus GPS data, the historical bus operation data of the same period and the same time period and the actually measured data of personnel, considering a plurality of factors of passenger transfer, two transfer station pairs of public traffic are converted, and a transfer model of big data is established, so that more accurate calculation of the departure station in transfer is completed; and verifying the correctness of the transfer model by methods such as video analysis and the like, and adjusting the weight value of the transfer factor according to the correctness.
Drawings
FIG. 1 is a block diagram of the transfer station calculation flow of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The transfer site estimation method based on the multi-metadata as shown in fig. 1 comprises the following steps.
At S1, the primary transfer site pair is acquired and site pairs for which multi-site transfers may exist are selected.
The first 100 bus stations are selected according to the scale of the urban buses and the sequence of the number of the transfer people at the peak time, wherein the first 50 bus station pairs are selected by the bus transfer buses and the first 50 bus station pairs are selected by the bus transfer track; for the 100 transfer station pairs, the bus route with the most transfer is selected (such as the straight line distance between a bus transfer station and a station close to the bus station of a certain bus route, if the nearest transfer station is within 50 meters of the transfer station, or the distance difference between the nearest transfer station and the station close to the next is more than 100 meters (wherein the parameter value of the distance difference between the nearest transfer station and the station close to the next can be adjusted according to the actual situation, such as 50 meters, 100 meters and the like), the multi-station transfer condition is considered to be absent, and a route and station list with larger passenger flow and possibly existing multi-station transfer is manufactured according to the selected multi-station transfer station pairs, wherein each item of the list is that the straight line distance between the station X and the station Y on the bus route AAA is A, B meters respectively from the transfer station Z.
And S2, filling a route and station list of multi-station transfer according to the passenger transfer habit and the transfer psychological factors.
For a bus line AAA from a station X to a transfer station Z, testing that the actual walking transfer distance is 200 meters, and the station Y is 60 meters away from the transfer station Z; the direction of the early peak passenger flow is from a station X to a station Y of a bus station, the average traveling time between the two stations of the bus is 4 minutes, the average traveling time is 80 meters per minute according to normal people (wherein the average traveling parameter is adjusted according to the proportion of passengers, middle-aged and old people, children, road conditions and weather reasons), and the actual walking distance from the station Y to a transfer station Z is converted into whether breakfast points exist at the roadside of the early peak, whether dish selling points or restaurants exist at the roadside of the late peak, and whether public toilets exist or not; giving grade 1 to grade 5 scores according to the safety of the channels in the transfer path, wherein the scoring standards comprise the existence of safety street-crossing channels for students, the existence of camera devices and the existence of special commands for street-crossing; scoring according to the convenience of the transfer passage, wherein the scoring standard comprises the conditions of existence of a sunshade pedestrian corridor, existence of an escalator in an elevation difference and barrier-free passing; the scores in the aspects are integrated to convert a total transfer channel evaluation coefficient, the coefficient is used for multiplying the transfer distance obtained before (the scores of the factors in the time periods such as morning and evening peak, average peak and the like are possibly different, for example, breakfast restaurants are considered in the morning peak, and dish buying points are considered in the evening peak), and therefore the algorithm transfer distance of the two stations before is obtained.
The parameters of average walking per minute are adjusted according to the proportion of passengers, middle-aged people and children, road conditions and weather reasons:
1. the card swiping number of the old people and the children (the type of the old card and the student card in the IC card accounts for more than 30 percent), the average walking distance is increased by 80 x 70% +120 x 30% according to the average walking of the old people and the children per minute and the same ratio;
2. the road condition is to multiply the average walking distance by a certain coefficient according to the factors such as the road width, the length of the ascending and descending steps and the like. For example, if the step of the existing upper ladder is 50 meters, the distance of the section is calculated to be 100 meters; if some road sections can only pass by two persons side by side, the transfer length of the road section is doubled;
3. if the distance of the road section is not covered by the sunshade facility, the distance of the road section is multiplied by a certain coefficient, such as 50 percent, in rainy days and other weather.
The grading is given according to the convenience of the transfer passage, the grading standard comprises a pedestrian corridor with or without sunshade, a staircase with or without electricity in the elevation difference, barrier-free passing conditions and the like, the grading is divided into 5 grades which are very convenient, general, inconvenient and very inconvenient, and the grading is divided into 5 grades, 4 grades, 3 grades, 2 grades and 1 grade in sequence.
In this example, the scoring criteria included the following 5,
the partial section of the transfer passage has a long-distance (such as more than 50 meters) pedestrian corridor without sunshade, and 1 to 2 minutes are reserved;
the transfer passage part has steps of ascending and descending with the height difference of more than 20 meters and labor-consuming manual judgment, no handrail and the like, and is buckled for 1 to 3 minutes;
the transfer passage part is not designed with a barrier-free passing facility or is buckled for 1 to 2 minutes.
And S3, calculating the transfer proportion of the get-off people at the adjacent stations according to the obtained algorithm transfer distance.
For example, in the simplest case of equal-proportion station population statistics, the number of stations X is 100 meters from station X to transfer station Z, and the number of stations Y is 150 meters from station Y to transfer station Z, so that the number of people from station X is 100 × 25 (100/100+150) out of 100 people to transfer station Z.
And S4, training the algorithm based on various bus high and low peak time periods according to the actually measured transfer number and the type of the transfer station pairs, and outputting a corresponding training model.
The actually tested transfer number and type classification standard of the transfer station pairs are classified according to transfer properties and selection tendency of passengers, wherein the classification according to the transfer properties comprises public-exchange buses and bus track-exchange types, and the classification according to the selection tendency of the passengers comprises commuting convenience type, commuting safety type, life and shopping convenience type and travel and entertainment convenience type.
And performing feedback training on the conversion algorithm based on various public traffic high-low peak-averaging time periods according to the actually tested transfer number and the type of the transfer station pairs, and further obtaining the personnel proportion of each transfer station pair in different time periods.
There are various methods for actually testing the number of passengers: for example, a high-definition camera is installed on a bus of a main transfer getting-on line, a high-definition camera is also installed at a bus stop or a track entrance of a transfer getting-on line, and getting-off personnel and transfer getting-on personnel are comprehensively identified and labeled according to methods such as human faces, human shapes, gaits, clothes and the like, so that the actual proportion of the number of the transfer people at different bus stops is obtained; in addition, a standard manual eye identification method can be adopted, a method of sending questionnaires and collecting questionnaires at a transfer station is adopted, and hardware technologies such as mobile phone signaling, mobile phone APP two-dimensional code scanning, wifi probes and the like can be adopted to label and identify personnel.
And (3) algorithm feedback training:
firstly, obtaining the type of a rough transfer station pair according to parameters (the convenience of the transfer channel, the safety of the transfer channel and the completeness of peripheral matching) and the type (bus or track) of the transfer station, wherein the parameters are related to the evaluation coefficient of the transfer channel; and then based on the actual personnel transfer proportion, considering the influence of the type of the transfer station pair on transfer, and adjusting the parameters such as convenience, safety, completeness and the like and the total transfer channel evaluation coefficient by adopting a sample-based regression algorithm until the transfer person number proportion of different stations calculated by the algorithm is consistent with an actual test value.
In the embodiment, a linear regression algorithm is adopted to adjust the parameters such as convenience, safety, completeness and the like and the total transfer channel evaluation coefficient. Taking the actual transfer proportion of the previous 3 similar days and same time periods as a sample, defining a linear equation set Xw as y, wherein X is a sample data matrix, and y is an expected value vector (namely a total transfer channel evaluation coefficient). The vector w includes the various parameters mentioned earlier: the algorithm is used for converting the distance (the initial weight of the parameter is higher), and various psychological conversion selection factors are selected.
The algorithm inputs are: a list of transfer station pairs, algorithm transfer distances among the transfer station pairs, and transfer psychological selection factors of the transfer station pairs; the proportion of people transferred in different periods of the actual test.
The algorithm output is: and (4) training a regression algorithm to obtain parameter values of transfer psychological selection factors of the transfer station pairs and total transfer channel evaluation coefficients.
The invention considers a plurality of factors (actual transfer distance + distance conversion of vehicle running time between stations, transfer channel convenience, transfer channel safety, peripheral matching completeness and the like) of passenger transfer based on the data of IC card swiping OD data, bus GPS data, historical bus operation data (such as average arrival time) in the same period, actually measured walking transfer distance/transfer channel safety convenience/passenger special tendency and the like, converting two transfer station pairs of public transportation (classified into bus transfer and bus track transfer types according to transfer properties, and classified into commuting convenience type, commuting safety type, living and shopping convenience type and tourism and entertainment convenience type according to passenger selection tendency), and establishing a big data transfer model so as to complete more accurate calculation of a departure station in transfer; the correctness of the transfer model is verified through methods such as video analysis and the like, and the weighted value of the transfer factor is adjusted according to the correctness, so that the calculation of the transfer station is more accurate, the actual passenger flow transfer information of high and low peaks is determined, departments such as city construction and traffic management are convenient to match corresponding transfer facility equipment, and public traffic operation and management departments perform more accurate transportation capacity and transportation volume matching, so that important public traffic urban indexes such as public traffic transfer efficiency, passenger satisfaction and the like are improved.
In this embodiment, the transfer station pair in S1 is obtained by using the conventional card swiping data of the bus IC card, and since the card swiping data of the bus IC card generally only includes the getting-on station and does not include the getting-off station, the getting-off station needs to be estimated according to the getting-on position of the bus or the track after the transfer and the previous bus route information. The traditional simple method is to find out the station which is closest to the geographical position of the station after transfer in all stations of the uplink and downlink of the bus line before transfer.
S1: the IC card of each passenger is swiped for recording, and according to the traditional algorithm, a bus stop which is closest to the transfer stop in the ascending or descending direction of the bus line is obtained and is used as a main getting-off stop;
s2: and taking the straight-line distance between the two stations (the presumed getting-off station and the transfer-on station) as a reference, multiplying the straight-line distance by a certain coefficient (such as 1.5 times or 2 times), finding out other bus stations in the bus line, which are close to the transfer station, and putting the bus stations into the transfer station pair.
In this embodiment, the route and station list for multi-station transfer is used to count the route number of the getting-off car before transfer, the station name of the car before transfer 1, the station number 1, the station transfer linear distance 1, the station name 2, the station number 2, the station transfer linear distance 2, the station name 3, the station number 3, the station transfer linear distance 3, the route number of the getting-on car after transfer, the station number of the getting-on car after transfer, and the remark item.
In the embodiment, the line number of the getting-off before the transfer is the bus line number of the passenger getting-off; the station name 1 before transfer is the first station name of the transfer condition possibly existing in the getting-off bus line; the station number 1 is the number of the station; the station transfer linear distance 1 is the linear distance between the station and a boarding station, the boarding line number after transfer is the track or bus line number of boarding of passengers, and the boarding station number after transfer is the track or bus station number of boarding.
In this embodiment, the passenger transfer habits are transfer behavior habits of passengers in different time periods.
In this embodiment, the psychological transfer selection factors include an actual transfer distance, distance conversion of inter-station vehicle travel time, convenience of transfer lanes, safety of transfer lanes, and completeness of peripheral matching.
In this embodiment, the transfer passage safety is classified into 5 grades of very safe, general, unsafe and dangerous according to the scoring standard, and the grades are sequentially classified into 5, 4, 3, 2 and 1.
In this example, the scoring criteria include the following 5,
the speed of people walking on part of the section of the transfer passage is less than half of the normal 1.2m/s due to the narrowness or congestion, and the number of the people walking is 1-2;
the transfer channel has pedestrian crossing channel, which is easy to have safety accident, no traffic light and other conditions, and the number of the channels is 1 to 3;
potential safety hazards such as falling rocks, falling objects, construction working faces and the like exist on two sides of the transfer passage, and the number of the potential safety hazards is 1-3;
the transfer passage has no street lamp at night, the underground passage has no camera device, and the events such as theft, robbery and the like happen in the near term, and the number of the events is 1 to 3;
the road is uneven, and children or old people fall or are injured due to channel hardware in recent times, and the number of the buttons is 1 to 3.
In this embodiment, the station pairs include a station pair for transferring buses and a station pair for transferring tracks.
Finally, although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that various changes and modifications may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (9)
1. The transfer site calculation method based on the multi-metadata is characterized by comprising the following steps,
s1, acquiring the main transfer station pairs, and selecting the station pairs where multi-station transfer is possible,
s2, filling in a route and a station list of multi-station transfer according to the passenger transfer habit and the psychological factors of transfer,
s3, calculating the transfer proportion of the get-off people at the adjacent station according to the obtained algorithm transfer distance,
and S4, training the algorithm based on various bus high and low peak time periods according to the actually measured transfer number and the type of the transfer station pairs, and outputting a corresponding training model.
2. The multivariate data-based transfer stop calculation method as defined in claim 1, wherein the transfer stop pairs in S1 are obtained by conventional bus IC card swiping data, and count a card swiping record of a bus-in stop of the bus IC card swiping and a card swiping record of a bus or a track taken after the transfer.
3. The transfer station calculation method based on multivariate data as defined in claim 1, wherein the line and station list for multi-station transfer is used to count the line number of the getting-off car before transfer, the station name 1 of the car before transfer, the station number 1, the station transfer straight-line distance 1, the station name 2, the station number 2, the station transfer straight-line distance 2, the station name 3, the station number 3, the station transfer straight-line distance 3, the line number of the getting-on car after transfer, the station number of the getting-on car after transfer, and remark items.
4. The multivariate data-based transfer stop calculation method as defined in claim 1, wherein the route number of getting-off before transfer is a bus route number of getting-off of a passenger; the station name 1 before transfer is the first station name of the transfer condition possibly existing in the getting-off bus line; the station number 1 is the number of the station; the station transfer linear distance 1 is the linear distance between the station and a boarding station, the boarding line number after transfer is the track or bus line number of boarding of passengers, and the boarding station number after transfer is the track or bus station number of boarding.
5. The multivariate data-based transfer site estimation method as defined in claim 1, wherein the passenger transfer habits are transfer behavior habits of passengers in different time periods.
6. The multivariate data-based transfer station calculation method as defined in claim 1, wherein the transfer psychological selection factors comprise actual transfer distance, distance conversion of inter-station vehicle travel time, transfer lane convenience, transfer lane safety and peripheral matching completeness.
7. The transfer site calculation method based on multivariate data as defined in claim 1, wherein the safety of the transfer passage is graded as 5 grades of very safe, general, unsafe and dangerous according to a grading standard, and the grades are sequentially graded as 5 grades, 4 grades, 3 grades, 2 grades and 1 grade.
8. The multivariate data-based transfer site estimation method as recited in claim 1, wherein the scoring criteria comprise the following 5:
the speed of people walking on part of the section of the transfer passage is less than half of the normal 1.2m/s due to the narrowness or congestion, and the number of the people walking is 1-2;
the transfer channel has pedestrian crossing channel, which is easy to have safety accident, no traffic light and other conditions, and the number of the channels is 1 to 3;
potential safety hazards such as falling rocks, falling objects, construction working faces and the like exist on two sides of the transfer passage, and the number of the potential safety hazards is 1-3;
the transfer passage has no street lamp at night, the underground passage has no camera device, and the events such as theft, robbery and the like happen in the near term, and the number of the events is 1 to 3;
the road is uneven, and children or old people fall or are injured due to channel hardware in recent times, and the number of the buttons is 1 to 3.
9. The multivariate data-based transfer stop calculation method as defined in claim 1, wherein the stop pairs comprise a stop pair for a public transport to transfer a public transport and a stop pair for a public transport transfer track.
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